Moving objects in tracked images often cause unstable object positioning due to change of color characteristics or shape of objects, for example, the distress caused by non-easy aligning with the target in applications of semi mouse's movement functions. In the past, smoothing technology of data points often uses complex smoothing algorithms and iterative techniques, for example, uses curve fitting and regression error analysis to modify the jitter curve, to immediately response the user's operation trajectory, and depict on the screen. This kind technique of smoothing data points need to solve simultaneous equations, thus computation amount and memory requirements are greater. This technique may achieve a good smoothing effect, but also induce the time delay.
FIG. 1 shows a schematic for a generation technique of computerized curve. As shown in the operation flow of FIG. 1, this technique performs an up-sampling (step 110) for the inputted data, and filters a sequence of samples after the up-sampling (step 120), and then performs a recursive curve interpolation to the filtered samples (step 130) to obtain a best curve. Some techniques of smoothing data points collect an amount of data for a period of time, then perform a data smoothing correction including such as performing noise reduction, smoothing the points of segment, and then curve fitting the points of segment to obtain a best curve.
The mouse cursor smoothing techniques such as one technology uses headset notations and controls the mouse cursor with a head facing direction. When the mouse cursor enters into a support position mode, this technology determines a new mouse cursor position based on a detection position and the average of the positions of a last point. Some tracking technologies for a mouse cursor controls the mouse cursor with the fingertips, and predicts a next cursor position according to the position and the speed of a current cursor, and performs an interpolation action at a low sampling frequency to maintain ease and smoothness of the cursor control. Some mouse cursor smoothing techniques use predicting cursor methods to alleviate timing jitter impact of network transmission for remote cursor control, and predict the next cursor position in accordance with the last cursor's position and speed, and the cursor's average acceleration.
Another technique of tracking a moving object improves the search strategy when tracking a moving target. As shown in FIG. 2, before detecting an object, this technology determines a search range based on the object's previous trajectory, such as the four data of the previous trajectory of a real object 202, and uses the direction of a least-square trend line 210 to predict the possible position 212 of this object at a next time point.
In the above mentioned curve smoothing technologies, some technologies achieve good smoothing effects, but do not immediately smooth data, which is unable to match the user expectation for the applications such as emphasizing real-time perception of gesture-like mouse trajectory. Some mouse cursor smoothing technology may be unable to overcome the problem of jitter due to more serious jitter of the data points, or to smoothly correct the trajectory when there is a gap between the cursor's true position and the predicted position. Some mouse cursor tracking technologies only predict the next cursor position with the current speed direction, and may cause a large error at a turning point. Some moving target tracking technologies predict an object target with a trend line, which may also cause a large error at a turning point. General curve generation technologies use the regression analysis to obtain only an orientation prediction, and may not directly provide the calculation of an absolute position.
Therefore, it is an important issue on how to design a trajectory smoothing mechanism with a balance between the global and the local trajectory trends, effective controlling the time delay within a small number of sampling points, and using simple trajectory smoothing techniques by simple algebra operations to achieve the effect of reducing the computation amount.